• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
• /
• pp.346-348
• /
• 2004

In this rarer, we introduce a new Fuzzy Polynomial Neural Networks (FPNNs)-like structure whose neuron is based on the Fuzzy Set-based Fuzzy Inference System (FS-FIS) and is different from that of FPNNs based on the Fuzzy relation-based Fuzzy Inference System (FR-FIS) and discuss the ability of the new FPNNs-like structurenamed Fuzzy Set-based Polynomial Neural Networks (FSPNN). The premise parts of their fuzzy rules are not identical, while the consequent parts of the both Networks (such as FPNN and FSPNN) are identical. This difference results from the angle of a viewpoint of partition of input space of system. In other word, from a point of view of FS-FIS, the input variables are mutually independent under input space of system, while from a viewpoint of FR-FIS they are related each other. In considering the structures of FPNN-like networks such as FPNN and FSPNN, they are almost similar. Therefore they have the same shortcomings as well as the same virtues on structural side. The proposed design procedure for networks' architecture involves the selection of appropriate nodes with specific local characteristics such as the number of input variables, the order of the polynomial that is constant, linear, quadratic, or modified quadratic functions being viewed as the consequent part of fuzzy rules, and a collection of the specific subset of input variables. On the parameter optimization phase, we adopt Information Granulation (IG) based on HCM clustering algorithm and a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized FSPNN (gFSPNN), the model is experimented with using gas furnace process dataset.

• 한국세라믹학회지
• /
• 제41권9호
• /
• pp.677-683
• /
• 2004

레이저 모체재료용 Nd:LLM (Nd:LiLa(MoO$_4$)$_2$) 단결정을 쵸크랄스키법으로 성장시켰다 성장된 Nd:LLM 단결정은 균열 등이 쉽게 발생하였는데, 균열의 원인은 상전이, 불합치용융, 구성성분의 화학적 불균질, 열적구조의 불균형 및 성장방향 등이 있다. 성장된 단결정의 TG-DTA 열분석 결과 상전이는 없었으며, XRD 회절분석에 의해 합치 용융체임을 확인하였다. Li$_2$O 성분의 휘발은 화학적 불균질에 중요한 원인이었다. 자체 제작된 저항발열로의 온도프로파일은 도가니 높이로 조절하였다 또한, Nd:LLM 결정은 성장방향에 따라 단결정의 성장에 영향을 받았으며, (101)의 방향의 성장에서 단결정의 품질이 가장 우수하였다. 성장된 단결정의 N$d^{3+}$ 이온의 분포 및 유효편석계수은 PIXE분석에 의해 수행되었다.

• Computers and Concrete
• /
• 제12권6호
• /
• pp.785-802
• /
• 2013

Ready-mixed soil material, known as a kind of controlled low-strength material, is a new way of soil cement combination. It can be used as backfill materials. In this paper, artificial neural network and nonlinear regression approach were applied to predict the compressive strength of ready-mixed soil material containing Portland cement, slag, sand, and soil in mixture. The data used for analyzing were obtained from our testing program. In the experiment, we carried out a mix design with three proportions of sand to soil (e.g., 6:4, 5:5, and 4:6). In addition, blast furnace slag partially replaced cement to improve workability, whereas the water-to-binder ratio was fixed. Testing was conducted on samples to estimate its engineering properties as per ASTM such as flowability, strength, and pulse velocity. Based on testing data, the empirical pulse velocity-strength correlation was established by regression method. Next, three topologies of neural network were developed to predict the strength, namely ANN-I, ANN-II, and ANN-III. The first two models are back-propagation feed-forward networks, and the other one is radial basis neural network. The results show that the compressive strength of ready-mixed soil material can be well-predicted from neural networks. Among all currently proposed neural network models, the ANN-I gives the best prediction because it is closest to the actual strength. Moreover, considering combination of pulse velocity and other factors, viz. curing time, and material contents in mixture, the proposed neural networks offer better evaluation than interpolated from pulse velocity only.

• 한국연소학회:학술대회논문집
• /
• 한국연소학회 2002년도 제24회 KOSCO SYMPOSIUM 논문집
• /
• pp.85-94
• /
• 2002

Computational flow dynamics(CFD) has been frequently applied to the waste incinerators to understand the flow performance for various design and operating parameters. Though it needs many simplifications and complicated flow models, the reasonability of its results is not fully evaluated. For example, the inlet condition is calculated from an arbitrarily assumed properties of combustion gas release from the waste bed, since the combustion in the bed is difficult to be predicted. In this study, the computational modeling and calculation procedures of CFD for the grate type waste incinerator were evaluated using comparative simulations. Though the assumption method on the generation of the combustion gas directly affected the temperature and gas species concentrations, the overall flow pattern was dominated by the secondary air jets. The gaseous reaction could be included by assuming the release of the products of incomplete combusion from the bed. However, the reaction effficiency cannot not be directly evaluated from the species concentration, since it is not possible to simulate the actual co-existence of fuel rich or oxygen rich puffs over the bed. In predicting the turbulence, the higher order model, such as Reynolds stress model, gave difference shape of local recirculation zones, but similar results was acquired from the standard $k-{\varepsilon}$ model. Introducing radiation model was required for accurate temperature prediction, but it also caused heat imbalance due to the fixed temperature of the inlet, i.e. the waste bed. Thus, the computational modeling procedures on incinerators and the analysis of the predicted results should be progressed carefully. Though not validated experimentally, current simulation method is capable of comparative evaluation on the flow-related parameters such as the furnace shape and secondary air injection using identical inlet conditions. Quantitative analysis using measures of the residence time and mixing is essential to compare the flow performance efficiently.

• 한국광학회지
• /
• 제7권4호
• /
• pp.348-356
• /
• 1996

기체의 순간 온도를 측정할 수 있는 펄스 레이저를 사용한 이동형 CARS 분광기를 제작하였다. 이 분광기의 측정 프로그램은 측정한 스펙트럼으로 부터 온도를 얻기 위하여 8가지의 빠른 온도 계산법과 최소제곱법을 포함하고 있다. 빠른 온도 계산법 중 두 가지는 최소제곱법 보다 계산 시간은 훨씬 덜 걸리면서 온도의 분산이 작은 측정 결과를 준다. CARS 온도의 정밀 정확도는 복사온도계를 기준으로 흑연관 흑체로에서 측정하였다. 1000K부터 2400K의 온도 영역에서 정확도는 .+-.2% 이내이고 정밀도는 가장 정밀한 결과를 주는 빠른 온도 계산법을 적용할 때 1600K에서 .+-.35K이다. 순간 온도 측정의 적용 예를 보이기 위하여 이 분광기를 정해진 조건에 있는 난류 연소의 측정에 적용하였다.

• Structural Monitoring and Maintenance
• /
• 제2권1호
• /
• pp.35-48
• /
• 2015

The main objective of this study is to evaluate the long-term performance of various concrete composites in natural marine environment prevailing in the Gulf region. Durability assessment studies of such nature are usually carried out under aggressive environments that constitute seawater, chloride and sulfate laden soils and wind, and groundwater conditions. These studies are very vital for sustainable development of marine and off shore reinforced concrete structures of industrial design such as petroleum installations. First round of testing and evaluation, which is presented in this paper, were performed by standard tests under laboratory conditions. Laboratory results presented in this paper will be corroborated with test outcome of ongoing three years field exposure conditions. The field study will include different parameters of investigation for high performance concrete including corrosion inhibitors, type of reinforcement, natural and industrial pozzolanic additives, water to cement ratio, water type, cover thickness, curing conditions, and concrete coatings. Like the laboratory specimens, samples in the field will be monitored for corrosion induced deterioration signs and for any signs of failureover initial period ofthree years. In this paper, laboratory results pertaining to microsilica (SF), ground granulated blast furnace slag (GGBS), epoxy coated rebars and calcium nitrite corrosion inhibitor are very conclusive. Results affirmed that the supplementary cementing materials such as GGBS and SF significantly impacted and enhanced concrete resistivity to chloride ions penetration and hence decrease the corrosion activities on steel bars protected by such concretes. As for epoxy coated rebars applications under high chloride laden conditions, results showed great concern to integrity of the epoxy coating layer on the bar and its stability. On the other hand corrosion inhibiting admixtures such as calcium nitrite proved to be more effective when used in combination with the pozzolanic additives such as GGBS and microsilica.

• 한국수소및신에너지학회논문집
• /
• 제20권5호
• /
• pp.384-389
• /
• 2009

KEPRI (Korea Electric Power Research Institute) has studied planar type solid oxide fuel cell (SOFC) stacks using anode-supported cells and kW class co-generation systems for residential power generation. In this work, a 1 kW SOFC system consisted of a hot box part, a cold BOP (balance of plant) part, and a hot water reservoir. The hot box part contained a SOFC stack made up of 48 cells, a fuel reformer, a catalytic combustor, and heat exchangers. Thermal management and insulation system were especially designed for self-sustainable operation in that system. A cold BOP part was composed of blowers, pumps, a water trap, and system control units. When the 1 kW SOFC stack was tested using hydrogen at $750^{\circ}C$, the stack power was about $1.2\;kW_e$ at 30 A and $1.6\;kW_e$ at 50 A. Turning off an electric furnace, the SOFC system was operated using hydrogen and city gas without any external heat source. Under self-sustainable operation conditions, the stack power was about $1.3\;kW_e$ with hydrogen and $1.2\;kW_e$ with city gas respectively. The system also recuperated heat of about $1.1\;kW_{th}$ by making hot water.

• Korean Chemical Engineering Research
• /
• 제54권6호
• /
• pp.775-780
• /
• 2016

최근 태양전지 산업에서는 효율과 더불어서 생산성을 높이고 원가를 절감할 수 있는 설계가 요구되고 있다. 생산성의 향상을 위하여 반응기의 크기를 키우면 기존의 8 inch 잉곳에서 12 inch 잉곳으로 생산이 가능하다. 또한 연속공정법을 사용하여 생산성 증대를 극대화 시킬 수 있다. 본 연구에서는 12인치 잉곳이 최적 컨디션의 수율향상을 위한 소비전력 감소와 생산성 향상에 관한 시뮬레이션을 진행하였다. 인출속도 별 계면 형상과 폰-미제스 스트레스, 온도구배, 소비전력을 비교하여 최적의 인출속도를 찾았다. 그 결과, 생산성 향상과 에너지를 절감할 수 있는 최적 공정 파라미터를 도출할 수 있었다. 이러한 연구는 실제 태양전지 산업에서 생산성 향상에 기여할 수 있을 것으로 기대 된다.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2004년도 추계학술대회 학술발표 논문집 제14권 제2호
• /
• pp.433-436
• /
• 2004

In this paper, we introduce a new Fuzzy Polynomial Neural Networks (FPNNS)-like structure whose neuron is based on the Fuzzy Set-based Fuzzy Inference System (FS-FIS) and is different from that of FPNNS based on the Fuzzy relation-based Fuzzy Inference System (FR-FIS) and discuss the ability of the new FPNNS-like structure named Fuzzy Set-based Polynomial Neural Networks (FSPNN). The premise parts of their fuzzy rules are not identical, while the consequent parts of the both Networks (such as FPNN and FSPNN) are identical. This difference results from the angle of a viewpoint of partition of input space of system. In other word, from a point of view of FS-FIS, the input variables are mutually independent under input space of system, while from a viewpoint of FR-FIS they are related each other. The proposed design procedure for networks architecture involves the selection of appropriate nodes with specific local characteristics such as the number of input variables, the order of the polynomial that is constant, linear, quadratic, or modified quadratic functions being viewed as the consequent part of fuzzy rules, and a collection of the specific subset of input variables. On the parameter optimization phase, we adopt Information Granulation (IC) based on HCM clustering algorithm and a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized FSPNN (gFSPNN), the model is experimented with using the time series dataset of gas furnace process.

• 한국지능시스템학회논문지
• /
• 제14권4호
• /
• pp.402-410
• /
• 2004

본 논문에서는 복잡하고 비선형적인 시스템을 위하여 최적 면역 알고리즘의 개선된 클론선택에 기반을 둔 최적FNN 설계방법을 제안한다. FNN은 퍼지추론의 간략 추론과 학습방법으로는 오류역전파 알고리즘을 하였고 멤버쉽함수의 파라미터, 학습률 및 모멘텀 계수들을 선정하기 위하여 개선된 클론 선택을 사용하는 방법을 도입하였다. 제안한 알고리즘은 생체의 면역반응에 기초를 둔 면역알고리즘의 클론선택을 기본으로 분화율을 조절하여 성능을 개선하였다. 그 과정을 통하여 다양한 항체들을 생성하고 목적함수나 제한조건과 같은 항원들에 대하여 가장 높은 친화도를 가지는 항체를 최적 항체로 선택하였다. 제안된 알고리즘의 성능을 평가하기 위하여 가스로공정과 교통경로선택 공정을 사용한다.